Class LogMulticlass
java.lang.Object
org.tribuo.classification.sgd.objectives.LogMulticlass
- All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable,com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>,LabelObjective
A multiclass version of the log loss.
Generates a probabilistic model, and uses an ExpNormalizer.
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionGenerates a newVectorNormalizerwhich normalizes the predictions into [0,1].com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenancebooleanReturns true.toString()valueAndGradient(int truth, SGDVector prediction) Returns aPairofDoubleand the supplied prediction vector.Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitMethods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable
postConfig
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Constructor Details
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LogMulticlass
public LogMulticlass()
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Method Details
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valueAndGradient
public com.oracle.labs.mlrg.olcut.util.Pair<Double, SGDVector> valueAndGradient(int truth, SGDVector prediction) Returns aPairofDoubleand the supplied prediction vector.The prediction vector is transformed to produce the per label gradient.
- Specified by:
valueAndGradientin interfaceLabelObjective- Parameters:
truth- The true label idprediction- The prediction for each label id- Returns:
- A Pair of the score and per label gradient.
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getNormalizer
Description copied from interface:LabelObjectiveGenerates a newVectorNormalizerwhich normalizes the predictions into [0,1].- Specified by:
getNormalizerin interfaceLabelObjective- Returns:
- The vector normalizer for this objective.
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isProbabilistic
Returns true.- Specified by:
isProbabilisticin interfaceLabelObjective- Returns:
- True.
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toString
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getProvenance
- Specified by:
getProvenancein interfacecom.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>
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